The present invention relates to a process control technique and, more particularly, to a feedback control method and feedback control device which perform disturbance recovery control by giving a manipulated variable to a controlled system so as to make the controlled variable recover to the set point at the time of application of a disturbance.
Conventionally, PID control has been known as a highly practical general control theory. As an advanced control theory like modern control theory, for example, simple adaptive control (SAC) has been known. According to either of the control theories, a manipulated variable MV is output as a control computation result to a controlled system so as to make a controlled variable PV recover to a set point SP at the time of application of a disturbance, and control computation is performed on the basis of a deviation Er between the controlled variable PV and the set point SP.
General PID control is a linear control theory, and is a control theory based on the assumption that a control system including a controlled system becomes a linear system. In practice, however, a controlled system does not have linearity in a strict sense, and PID control can tolerate slight nonlinearity. For example, heating by a halogen lamp in a RTP (Rapid Thermal Process) used in a semiconductor manufacturing apparatus is a system with strong nonlinearity to which PID control cannot be simply applied. In this case, even PID control can be used if only the stability of a control system is to be pursued. However, PID control cannot cope with operation under the condition that a fast temperature rise and a response waveform with a little overshoot are required as in an RTP.
Assume that the nonlinearity of a control system can be approximated by a characteristic curve K shown in FIG. 14. In this case, when the controlled variable PV is to be made to recover to the set point SP with a fast temperature rise (fast disturbance recovery) at the time of application of a temperature-decreasing disturbance, the manipulated variable MV (heating output) becomes 100% at the time point when the deviation Er between the set point SP and the controlled variable PV is large. As a consequence, an average process gain characteristic curve has a large gradient as indicated by “Kav1” in FIG. 14. As the temperature rises and the deviation Er decreases, the manipulated variable MV decreases to, for example, about 20%. In this case, an average process gain characteristic curve becomes another characteristic curve having a small gradient as indicated by “Kav2” in FIG. 14.
When the PID parameters of a PID controller are adjusted in conformity with specifications for fast disturbance recovery, and the PID controller is applied to a strong nonlinearity system like that shown in FIG. 14, a temperature rise curve (disturbance recovery waveform) becomes like a curve PV in FIG. 15. That is, in the first half period of response, overshoot occurs in the controlled variable PV as in a case wherein a controlled system with an excessively large process gain is controlled, whereas in the second half period of response, control operation occurs such that the controlled variable PV follows up to the set point SP at extremely low speed as in a case wherein a controlled system with an excessively small process gain is controlled. As a result, a temperature rise curve like that shown in FIG. 15 appears. This control is not suitable for a controlled system for which a response waveform with a little overshoot is required as in the case of a semiconductor manufacturing apparatus. In addition, adjustment of PID parameters falls out of the range of a linear control theory, and hence is very difficult to realize.
An advanced adaptive control theory such as simple adaptive control (SAC) is designed to automatically correct the internal parameters of a control computation unit so as to always obtain proper control characteristics with respect to variations in the process gain characteristic of a controlled system. However, for proper automatic correction (adaptive operation) for the internal parameters, control computation must be performed by a sufficient number of times in a transient state. In fast disturbance recovery, the time required for a temperature rise is about 1.0 to 1.5 sec, as shown in FIG. 16A. If, therefore, the control cycle is 50 msec, the number of times of control computation in disturbance recovery is about 20 to 30.
The number of times of control computation allowed to follow up a change in process gain due to strong nonlinearity characteristics under such conditions is about two to three at best, as shown in FIG. 16B. This number of times of control computation is simply too small to make adaptive operation properly function. At practical level, a technique based on an advanced adaptive control theory can obtain the stability of control, in the end, at best, but cannot make a controlled system with strong nonlinearity characteristics smoothly achieve fast disturbance recovery. This technique is substantially directed to only ensure stability in application to not only fast disturbance recovery but also other operations. Furthermore, there are no guidelines for practical standards concerning settings of many parameters to be set in advance for proper adaptive operation.
As described above, according to the conventional PID control theory, proper disturbance recovery control cannot be realized for a controlled system with strong nonlinearity, and it is difficult to adjust PID parameters.
In addition, according to an advanced adaptive control theory such as simple adaptive control (SAC), in a controlled system with strong nonlinearity characteristics, when the controlled variable is to be made to recover to the set point at high speed, since the number of times of control computation allowed is too small to make adaptive operation properly function, proper disturbance recovery control cannot be realized. In addition, it is difficult to adjust parameters.